TECH TUESDAY: Tabb Unveils Data and Analytics Research Practice

Research drives the bus. And now so does data.

Given the increased complexity of trading and reliance on research and data, Wall Street consultancy and news aggregator Tabb Group announced it has formed a new Data and Analytics (DnA) research practice. The goal of the new group is to help both the buy- and sellside navigate these burgeoning pieces of the trading puzzle and make sense of how to use them.

TABBs DnA research is designed to help institutional investment firms understand the latest trends, methodologies and solutions critical to achieving best-practices in data management capabilities. It will also assist technology sales and marketing organizations understand specific requirements and uses cases within capital markets firms.

This follows the recent foray of Greenwich Associates, another industry consultancy, into the market structure and technology advisory business. Under the banner Greenwich Information Services, this group is headed by Kevin McPartland, a former Tabb principal. GIS will leverage its research capabilities that have been built on its buyside connections to help clients navigate changes in market structure driven by regulatory and technological trends and shifts. GIS was formally announced back in September.

Two months ago, McPartland told Traders Magazine that Greenwichs Information Services division started to build-up in this area about two years ago. McPartlands unit, which is dentifying trends and decipher how regulations and IT changes are impacting markets, should be available to all in the equities marketplace, not just the institutional buy- or sellside.

The foray into market structure data and analytics underscores the buyside traders recent need for additional help in understanding and processing the reams of data thrown at them in the course of the trading day. In the current tight commission environment, efficiency needs to be wrung out of all the traders processes; not just during the execution, but during pre- and post-trade as well. Enter Tabb and Greenwich.

The Tabb Group announcement was made today during an industry event, the MarketTech 2013 conference in New York City. Tabb wrote in a press release that it believes that technology is a critical pillar for the reinvention of the capital markets, and that in combination with shifts in employee skill sets and process re-engineering, new weapons of mass disruption, such as data and the increased amount of it, need to be employed broadly to right-size business models, augment capabilities and harvest fleeting, complex opportunities.

Senior analyst Paul Rowady, in a new commentary posted to Tabbs website, said that TABB recognizes that the journey from raw data to analytics to intuitive pictures is among one of the most important for firms competing to survive – and thrive – in the new capital markets world. As a result, were galvanizing a new focal point in the form of a Data and Analytics (DnA) practice, to help guide the entire capital markets ecosystem improve its return on data management investments.

Tabb announced its list of research topics it plans to cover for the remainder of this and next:

Text Analytics: A New Capital Markets Science

Managed Services: Driving Value by Minimizing Risk and Enabling Innovation

OTC Risk Analytics for the Buyside

The Fixed-Income Market Data Landscape: Sources, Sizes and Performance Needs

Big Data Meets Big Memory: Bigger Workloads, Faster

The Dynamic Data Thermos

The Metadata / Semantic Library: At the Heart of Next-Generation Data Fluency

The Cost of Data Distribution/Dissemination

Computational Destinations: The Cost Crunch

High-Performance Cloud: Fast & Shared

Hadoop: What does it mean for Capital Markets?

The Foundations of the Hybrid Enterprise

Global Markets on Demand: Revisited

Global Fire Hose: How much data is there?

The volume, velocity, diversity and complexity of data flows in financial markets today have already far surpassed human capacity to absorb them, Rowady said. Machines now handle most of the processing of raw data, but only after humans teach the machines what to do. In order to teach machines what to do next, humans first need to understand what to do next. And the only way for this to happen is for humans to be able to improve our understanding – to augment their cognition – of what lies within the data.